Update app.py

app.py

@@ -238,62 +238,62 @@ Overall Performance Level: [Exemplary (85-100%)/Proficient (70-84%)/Developing (
         return summary
 
     def parse_assessment_scores(self, assessment_text):
-    """Parse assessment text to extract scores"""
-    
-    # Method 1: Look for "Status: OBSERVED" vs "Status: NOT OBSERVED" patterns
-    import re
-    
-    # Find all status lines
-    status_pattern = r'Status:\s*(OBSERVED|NOT OBSERVED)'
-    matches = re.findall(status_pattern, assessment_text, re.IGNORECASE)
-    
-    # Count only "OBSERVED" (not "NOT OBSERVED")
-    observed_count = sum(1 for match in matches if match.upper() == "OBSERVED")
-    
-    # If no matches found with Status: pattern, try alternative parsing
-    if len(matches) == 0:
-        # Alternative: Look for competency lines with OBSERVED/NOT OBSERVED
-        lines = assessment_text.split('\n')
-        observed_count = 0
+        """Parse assessment text to extract scores"""
         
-        for i, line in enumerate(lines):
-            # Look for competency indicators followed by status
-            if 'Competency' in line and i + 1 < len(lines):
-                next_line = lines[i + 1]
-                # Check if the status line indicates OBSERVED (not NOT OBSERVED)
-                if 'OBSERVED' in next_line.upper() and 'NOT OBSERVED' not in next_line.upper():
-                    observed_count += 1
+        # Method 1: Look for "Status: OBSERVED" vs "Status: NOT OBSERVED" patterns
+        import re
         
-        # If still no matches, use a more robust pattern
-        if observed_count == 0:
-            # Count lines that say "OBSERVED" but not "NOT OBSERVED"
-            for line in lines:
-                # Clean line for better matching
-                clean_line = line.strip().upper()
-                if clean_line.startswith('STATUS:'):
-                    if 'NOT OBSERVED' in clean_line:
-                        continue
-                    elif 'OBSERVED' in clean_line:
+        # Find all status lines
+        status_pattern = r'Status:\s*(OBSERVED|NOT OBSERVED)'
+        matches = re.findall(status_pattern, assessment_text, re.IGNORECASE)
+        
+        # Count only "OBSERVED" (not "NOT OBSERVED")
+        observed_count = sum(1 for match in matches if match.upper() == "OBSERVED")
+        
+        # If no matches found with Status: pattern, try alternative parsing
+        if len(matches) == 0:
+            # Alternative: Look for competency lines with OBSERVED/NOT OBSERVED
+            lines = assessment_text.split('\n')
+            observed_count = 0
+            
+            for i, line in enumerate(lines):
+                # Look for competency indicators followed by status
+                if 'Competency' in line and i + 1 < len(lines):
+                    next_line = lines[i + 1]
+                    # Check if the status line indicates OBSERVED (not NOT OBSERVED)
+                    if 'OBSERVED' in next_line.upper() and 'NOT OBSERVED' not in next_line.upper():
                         observed_count += 1
-    
-    total_competencies = 18
-    percentage = (observed_count / total_competencies) * 100 if total_competencies > 0 else 0
-    
-    # Professional color scheme with better contrast
-    if percentage >= 85:
-        level = "Exemplary"
-        color = "#0F766E"  # Deep teal
-    elif percentage >= 70:
-        level = "Proficient"
-        color = "#1E40AF"  # Professional blue
-    elif percentage >= 50:
-        level = "Developing"
-        color = "#EA580C"  # Professional orange
-    else:
-        level = "Needs Improvement"
-        color = "#B91C1C"  # Deep red
-    
-    return observed_count, total_competencies, percentage, level, color
+            
+            # If still no matches, use a more robust pattern
+            if observed_count == 0:
+                # Count lines that say "OBSERVED" but not "NOT OBSERVED"
+                for line in lines:
+                    # Clean line for better matching
+                    clean_line = line.strip().upper()
+                    if clean_line.startswith('STATUS:'):
+                        if 'NOT OBSERVED' in clean_line:
+                            continue
+                        elif 'OBSERVED' in clean_line:
+                            observed_count += 1
+        
+        total_competencies = 18
+        percentage = (observed_count / total_competencies) * 100 if total_competencies > 0 else 0
+        
+        # Professional color scheme with better contrast
+        if percentage >= 85:
+            level = "Exemplary"
+            color = "#0F766E"  # Deep teal
+        elif percentage >= 70:
+            level = "Proficient"
+            color = "#1E40AF"  # Professional blue
+        elif percentage >= 50:
+            level = "Developing"
+            color = "#EA580C"  # Professional orange
+        else:
+            level = "Needs Improvement"
+            color = "#B91C1C"  # Deep red
+        
+        return observed_count, total_competencies, percentage, level, color
 
     def generate_pdf_report(self, assessment_text):
         """Generate a PDF report from the assessment text"""